In the last decades, the interest toward wireless communications has greatly increased. Such an interest has pushed the development and refinement of wireless protocols and technologies. All types of wireless communications have one thing in common: they allow data transmission over the air. However, transmitting data over the air introduces issues such as interference, distortion and multipath propagation. To overcome such issues, multiple techniques for treating received data signals have been developed in combination with more robust modulation techniques. Some of the mostly used modulation techniques include Orthogonal Frequency Division Multiplexing (OFDM) and Orthogonal Frequency Division Multiplexing Access (OFDMA).
More particularly, in OFDM and OFDMA, multiple signals are modulated on different sub-carriers that operate on different frequencies. These sub-carriers are then added together to create a composite signal. The composite signal is afterwards modulated onto a single carrier frequency for transmission over the air from one device to another. The essence of an OFDM signal lies, however, in the way the sub-carriers are placed with respect to each other. The sub-carriers are chosen so as to have orthogonally spaced sub-carriers to avoid inter-carrier interference.
To further increase the accuracy of a decoded OFDM signal, a method called soft-demapping is applied to the signal. In OFDM receivers, a soft-demapper module performs constellation demapping by indicating, for each bit of a symbol, the level of confidence that a bit is either a “0” or a “1”. The output of the soft-demapper is used by a decoder. This method provides a high level of accuracy when the channel is frequency non-selective. For instance, white noise and flat fading generates on average the same SNR (Signal to Noise Ratio) for all sub-carriers in a signal.
However, intersymbol interference resulting from multipath propagation affects each sub-carrier differently and is therefore considered as being frequency selective: each sub-carrier has a different level of SNR depending on the amplitude of the sub-carrier channel response. A generalized method for symbol estimation such as soft-demapping leads to a low level of accuracy when in presence of frequency selective interference. It is therefore necessary to use other methods for establishing the confidence level of symbols in each sub-carrier.
Many have proposed various methods for establishing the CSI indicator for each sub-carrier. One of the ways used for determining the CSI indicator is through an interpolation of an estimated SNR for pilot carriers. The estimated SNR for each pilot carrier is established by processing the pilot carriers through a CSI computation module of the OFDM receiver. The CSI computation module is then capable of establishing the estimated SNR for each sub-carrier. The generated CSI estimate for each sub-carrier is then taken into consideration by the decoder.
Several have tried to combine the SNR estimates originating from both frequency non-selective and frequency selective interferences. In Kim, US patent application 2006/0182015, an apparatus and method of estimating a CPE (Common Phase Error) uses data and pilot sub-carriers for establishing a CSI indicator. For each sub-carrier, a CSI indicator is estimated from the Discrete Fourier Transform (DFT) signal. The CPE is generated from a CPE estimation unit that uses as input an equalized signal and the CSI indicator. A Soft-Demapper then demaps the CPE compensated equalized signal for decoding.
In the same line of thought, in Gupta et al., US patent application 2006/0222097, is disclosed a method that teaches a SNR based selection filter. The method further teaches the use of the filter in combination with a DFE (Decision Feedback Equalizer) with which is then combined a WCSI (Weighted Channel State Information). The WCSI is used by the decoder, which is modified to give decoding weights to symbols proportional to channel estimation. The channel estimation is performed in a frequency domain.
Although the systems presented by Gupta et al. and in Kim may provide a respectable level of accuracy in the decoded data, several computational modules must be added to the conventional OFDM receiver. It is needless to say that the addition of computational modules decreases the efficiency of the OFDM receiver. As OFDM receivers are integrated in various communication units such as devices that generate high levels of data traffic, it is thus important that the OFDM receiver manages to maintain its efficiency and consume as little power as possible. It would therefore be useful to have an OFDM receiver that is capable of increasing the level of data accuracy regardless of the environment or signal propagation conditions while remaining efficient and consuming as little power as possible.